Hierarchical biological modelling system and method

ABSTRACT

A hierarchical biological modelling system and method provides integrated levels of information synthesized from multiple sources. An executable model of a biological system is developed from information and structures based on the multiple sources. The model is balanced to ensure that it matches the information and structures. Once the model is created and balanced it can be used to provide insight into phenomena at the cellular, or subcellular level, as well as phenomena at the patient, organ and system levels. From this information clinical trials can be emulated, biologic targets for drug development can be identified and subcellular phenomena over time can be observed. The model provides an integrated view of a multi-variable biological system.

RELATED APPLICATIONS

This is a continuation of application Ser. No. 08/422,175, filed on Apr.14, 1995, now U.S. Pat. No. 5,657,255.

COPYRIGHT NOTIFICATION

Portions of this patent application contain materials that are subjectto copyright protection. The copyright owner has no objection to thefacsimile reproduction by anyone of the patent document or the patentdisclosure, as it appears in the Patent and Trademark Office patent fileor records, but otherwise reserves all copyright rights whatsoever.

FIELD OF THE INVENTION

The present invention relates to modelling, and in particular a dynamicinteractive modelling system which models biological systems from thecellular, or subcellular level, to the human or patient populationlevel.

BACKGROUND OF THE INVENTION

New drug development is typically motivated by the need or opportunityto affect an individual's quality of life. Development focuses onidentifying and selecting compounds having the potential to affect oneor more mechanisms thought to be critical in altering specific clinicalaspects of the disease processes.

Drug development is also motivated by exciting research data regardingcellular and subcellular phenomena. Very often, however, the dataconsiders only an isolated and rather narrow view of an entire system.Such data may not provide an integrated view of the complete biologicalsystem. Moreover, the narrow findings reported are not always entirelyaccurate when translated to the whole body level.

Current methods of obtaining data for biological processes requireextremely time consuming laboratory experiments that lead to animalexperiments and clinical trials. From these trials and experiments, dataarc obtained which usually focus on a very narrow part of the biologicalsystem. While conclusions may be drawn by assimilating experimental dataand published information, it is difficult, if not impossible, tosynthesize the relationships among all the available data and knowledge.In fact, the human mind is only capable of considering approximatelyseven factors at one time, and lacks the ability to accurately accountfor feedback in systems over time. Furthermore, incorporation ofmultiple uncertainties, as well as feedback, often leads tooversimplification or artificial partitioning by the human mind, whichcan result in misleading conclusions.

Previous modelling efforts for designing drugs have typically focused oncreating molecular models of a proposed drug or drug target. Themolecular models are designed to meet certain criteria believed to havea desired impact at the molecular level. The desired impact is generallydetermined by studying the biology of interest at the molecular levelthrough laboratory experiments.

Drugs designed using this type of modelling either represent refinementsof existing drugs or an attempt to develop a drug for a new part of thedisease that was suggested from conclusions drawn from clinical trialsand laboratory experiments. The complexity of the information, however,does not always provide a clear and consistent picture from whichaccurate conclusions can be drawn, and the resulting designer drugsoften reflect this inaccuracy.

Typically, designer drugs often meet design goals related to particularconclusions and observations at a cellular or subcellular level, but mayfail when clinically tested because the design process fails to takeinto account the nuances of the complete biological system. Only afternumerous costly trial-and-error clinical trials, and constantredesigning of the clinical use of the drug to account for lessonslearned from the most recent clinical trial, is a drug having adequatesafety and efficacy finally realized. This process of clinical trialdesign and redesign, multiple clinical trials and, in some situations,multiple drug redesigns requires great expense of time and money. Eventhen, the effort may not produce a marketable drug.

This scenario has a chilling effect on efforts to produce a drug foranything but an extremely large segment of the population. Biologicalabnormalities which may be treatable by a drug may not explored becausethe potential market for the drug does not justify the expenditure ofresources necessary to design, test, and obtain approval for the drug.

Because of the high initial costs of clinical trials, experimentation,and government approval, drug development today focuses on large patientpopulations. Even then, development is extremely speculative. Insummary, the overhead for drug development is very high, and difficultto justify except for the largest of patient populations.

Clinical trials typically are designed to isolate on a single variable,and use a placebo control group as a baseline from which the variable ismeasured. Observations from a clinical trial attempt to draw conclusionsfrom apparent differences between the control group and the experimentalgroup. These observations, however, do not take into account themulti-variable dynamic nature of the patients individually, or as agroup. Such variations usually increase the variability in the data andrequire large test populations to deal with the variability in anappropriate statistical manner.

A typical cycle for a clinical trial can require years; designing thetrial may take six months, performance of the trial may take a year, andanalysis of the results may take yet another six months. After years oftesting, the results still may be subject to suspicion. Additionally, atrial may be one of several ongoing trials necessary to address thevariables associated with a particular area of investigation.

Due to the single-variable nature of the drug development business, thereported data results in a great degree of uncertainty. Each studyprovides a very narrow, often debatable, view of the complete system.Ultimately, the different studies fail to provide a complete picture ofthe entire biological system, since the studies develop information fromdifferent perspectives and assumptions.

What is needed then, is an alternative system and method whichefficiently discovers and conveys information regarding complexbiological systems.

SUMMARY OF THE INVENTION

It is therefore an object of the present invention to provide a systemand method for modelling biological systems and disease processes.

It is another object of the present invention to provide a system andmethod for modelling biological systems in a manner reflecting thedynamic and multi-variable nature of the systems.

It is still another object of the present invention to provide a systemand method for representing a biological system in a hierarchical mannerof varying levels of complexity.

It is yet another object of the present invention to provide ahierarchical modelling system which is interactive.

It is a further object of the present invention to provide a method fordrug development which relies upon the present modelling method andsystem.

Another object of the present invention is the provision of a method fordeveloping clinical trial designs through the application of the presentmodelling method and system.

These objects are achieved by the present dynamic computer-based systemthat simulates interrelated biological findings and hypotheses at thecellular and subcellular levels to better predict and successfully alterclinical outcomes manifested as signs and symptoms of disease. Thepresent invention provides an interactive tool to help identify new drugtargets, to develop a better understanding of key biological mechanisms,and to assess the potential for influencing important clinical outcomes.The functional computer model integrates all of the biologicrelationships that are known to exist and that are relevant to theparticular disease process of interest. The integration provides adynamic executable model reflecting changes over time at each level ofthe system hierarchy. For example, the course of a particular diseaseprogression, and impact of a particular treatment on the progression,are demonstrable by the system.

The present system and method recognize that the body is organized inlevels of increasing complexity from the subcellular level to thecellular level to the tissue/organ systems to the whole external body ofan intact animal. At every level, interrelated and redundant mechanismswith complex feedback loops produce responses that influence theclinical outcomes. Many of these mechanisms are modified in individualpatients by genetic and environmental factors.

As mentioned above, current drug development is typically the result ofnew, exciting observations at the cellular and subcellular level. Thepresent invention realizes that although these observations identifypotential targets for new drug discovery, the targeted mechanisms arerarely independent. A change in one system can have cascading effectsdue to complex interrelationships at higher levels of complexity thatwill determine drug efficacy, side effects, and drug developmentprofiles.

Other objects and advantages of the present invention will becomeapparent from the following detailed description, which when viewed inconjunction with the accompanying drawings, sets forth the preferredembodiment of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing an example of a software hierarchywhich could be used as a basis for model creation;

FIG. 2 is a block diagram showing linked fundamental model units onlevels, and linking of fundamental model units between levels;

FIG. 3 is a block diagram showing the typical display and structure of alow level of the model;

FIG. 4 is a block diagram showing what could be considered aninteractive display and structure of a middle level of the hierarchy;

FIG. 5 is an example of an interactive display and structure of anotherlevel of the model hierarchy;

FIG. 6 shows the overall flow of operations for collecting information,developing a representation of the system, making a model, running themodel and using the model;

FIG. 7 shows an example of a knowledge diagram;

FIG. 8 shows an example of linking together two models; and

FIG. 9 represents a computer system on which the present invention maybe practiced.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The detailed embodiment of the present invention is disclosed herein. Itshould be understood, however, that the disclosed embodiment is merelyexemplary of the invention, which may be embodied in various forms.Therefore, the details disclosed herein are not to be interpreted aslimiting, but merely as the basis for teaching one skilled in the arthow to make and/or use the invention.

The present invention provides a method and apparatus which allowscritical integrated evaluation of conflicting data and alternativehypotheses. A model is developed representing not just chemicalprocesses at the lowest level, but the larger biological systemsimpacting on these chemical processes. This provides a multi-variableview of the system, as opposed to the old single variable system. Thepresent invention also provides cross-disciplinary observations throughsynthesis of information from two or more disciplines into a singlemodel, or through linking two models which respectively representdifferent disciplines.

The model can be built to simulate individual patients or specificgroupings of patients, and not the general population as a whole. Byproviding individual patient simulations, individual susceptibility andenvironmental factors can be directly linked to the biology and clinicaloutcomes. Specific grouping patient simulations also provides a way ofexploring patterns of patient-level factors that may influence biologicbehavior. The model also incorporates critical anatomic considerationswhich are relevant to the biological area or system of interest. Byassessing localization of specific mechanisms associated with theanatomy, certain constraints on biological interactions are revealed.

The model is hierarchical, and reflects the particular system andanatomical factors relevant to the issues to be explored by the model.The level of detail at which the hierarchy starts, and the level ofdetail at which the hierarchy ends, are largely dictated by theparticular intended use of the model. Because drugs often operate at thesubcellular level, the lowest level of the hierarchy will often be thesubcellular level. And because the individual is the most common entityof interest with respect to the safety and efficacy of the drug, theindividual in the form of clinical observables is often represented atthe highest level of the hierarchy, as depicted in FIG. 3, element 66.

Within each level of the hierarchy there are fundamental model units(FMU) which represent relevant biological information and processes atthat particular level. An FMU typically reflects a particularrelationship among several factors affecting the level. Any one level istypically comprised of multiple FMUs, which may be linked together. Thelevels, in turn, are linked together so that data and informationdeveloped at one level are passed on to other levels in accordance withthe model.

FIG. 1 is a block diagram showing an example of a software hierarchywhich could be used as a basis for model creation. The lowest level isthe basic model development tool 124, such as EXTEND™ by Imagine That|

According to the disclosed embodiment, a second level 122 includes cellpools and regulators. A cell pool is a population of cells of aparticular type or which is in a particular state. Regulators controlinflow and outflow of a cell pool. The next level 120 is comprised ofcell pool controllers and chemical production. A cell pool controller isa cell pool and its regulators (in the form of chemical levels in theenvironment of the cell pool).

The next level 118 is comprised of cell classes. A cell class is a groupof related cell controllers, usually of a particular cell type but indifferent states, plus the chemicals that are produced by the variouscell pools, and the chemicals that control the production.

It should be noted that levels 118, 120 and 122 are based on KnowledgeDiagrams (KDs) described in greater detail below. Put another way, theselevels implement the KDs which are developed in the preliminary stagesof model design. This relationship is indicated by box 126.

The next level 116 represents system function, or system/bodyresponse/function, which is a collection of cell classes constituting acoordinated biological function, such as immune response or boneremodelling. Finally, level 114 represents the model which is the sum ofthe parts below. The model is a collection of system/bodyresponse/function representing key components of the biologicalprocesses (e.g., disease) of interest.

An example of the hierarchy could be cell pools and regulators at thelowest level, cell classes and chemical production at the next level,cell types at the next level, human/anatomical response at the nextlevel, manifestation of the disease at the next level, and clinicalsigns and symptoms at the final level. This results in a top layer whichis always linked to the critical clinical outcomes.

FIG. 2 is a block diagram showing linked Fundamental Model Units (FMUs)140 on levels 132, 134, 136 and 138, and linking of FMUs 140 betweenlevels. The lines linking FMUs 140 on and between levels 132, 134, 136and 138 represent relationships between the individual entities. WhileFIG. 2 shows four levels, it should be kept in mind that a model may becomprised of one or more levels, depending on the complexity of thesystem being modelled. Typically, the model will consist of three ormore levels, but a one or two-level model is certainly possible.

FIG. 3 is a block diagram showing a typical display and structure 68 ofthe entities making up the highest level of the model. This particularexample level comprises Patient 56, Site A Surface 58, Bacteria A 52,Bacteria B 54, Site A Inflammation 60, Output 62, Attachment Site 64,and Clinical Obscrvables 66. It should be kept in mind that the drawingsin the present specification only convey information regarding makingand using a hierarchical biological model, and are not intended to bebiologically precise.

A typical entity on a level may be comprised of one or more inputs, agraphic element representing synthesis of those inputs, and one or moreoutputs. An entity may also have only one or more outputs or one or moreinputs. Taking Bacteria A 52 as an example, the inputs are representedby the vertical blocks to the left of the double circles, the doublecircle graphic represents synthesis of the inputs, and the verticalblocks on the right represent outputs of the synthesis.

The information on display 68 is interactive in that a user is able toalter not only the particular elements shown, but can also modify theunderlying information which the elements represent. For example, if theuser were to select and open the double circles file representingBacteria A 52, a lower level representation of the model would pop up onthe screen, allowing the user to examine more detail about how thesynthesis represented by the double circle graphic is accomplished. Thephysical appearance of the graphics representing synthesis from inputsto outputs can be customized by the user to convey meaning regarding theparticular synthesis being performed and represented by the graphic. Thesynthesis may, for example, be a mathematical manipulation of the datainput to the block.

Attachment Site A 64 shows several functional blocks attached to form alarger entity 64. Note also that some of the outputs on this level arealso inputs on this level. For example, Allergy hx output from Patient56 is an input in Attachment Site A 64. This internal interaction is afeature of biological systems which makes an understanding of a singleentity only of limited use. The present invention combines theseentities to create a complete model of the biological system.

Output block 62 provides for visual output of the variables which areinput. This provides a user with the ability to grab various inputs oroutputs and display them together in graph form, or in some othermeaningful way which conveys the relationship among the data.

It should be kept in mind that while each numbered entity could beconsidered an FMU, an FMU could be comprised of a group of suchentities. The phrase "Fundamental Model Unit" is only intended to be aconvenient terminology for referring to entities making up the model.

FIG. 4 is a block diagram showing an interactive display and structure94 of the level of the hierarchy below the level shown in FIG. 3. Thisparticular example shows an entity which represents the Site AInflammation 60. This level could be comprised of a variety of entitiessuch as Cell Poll Controllers 82, ADD Function Block 84, ChemicalProduction 86, Threshold Block 88, and Cell Pool Controllers 92. Thegeneral structure demonstrated by this level is that of Cell Classtaking chemical inputs developed from a variety of sources, andoutputting chemical levels produced by the various cell types. Asdiscussed with respect to FIG. 3, FIG. 4 represents interactive graphicsentities, as well as the overall biology of the level. Each of thegraphical entities 82, 84, 86, 88 and 92 represent a synthesis of therespective inputs to each entity. The "boxed" inputs, such as thatrepresented by 96, are data paths connected to data developed fromanother level.

FIG. 5 is an example of an interactive display and structure 102 ofanother level of the hierarchy of a cell pool.. The use of Random NumberGenerators 100 provide a means for generating some of the materialvariation within the model that is found in biological systems. Suchvariation can support statistical analysis over a population when themodel is run many times.

The model is capable of integrating complex interactions over time,which clarifies negative and positive feedback mechanisms that arecritical to the homeostasis of an organism. Without the temporalintegration it is not possible to identify the true regulatory nature ofbiological interactions.

DEVELOPMENT OF PURPOSE FOR A MODEL

The initial impetus for model development arises from recognition of aparticular problem to be solved in the drug development field. A clientmay specify the particular disease aspect to model, such as the need toidentify a new target for drug development or the need to design aclinical trial for an existing drug. From this information adetermination may be made as to which building blocks must be included.For example, a client may wish to identify whether a specific input ofan individual's biology is linked to a genetic variation. In thissituation, the model is developed to include the biology to a level ofdetail necessary to link the variations in clinical outcomes to thevariation in a patient's basic biology. As a result of this linkage, thecause of the biological variation can potentially be traced from thebiological variation back to a specific genetic variation.

IDENTIFICATION OF RELEVANT FACTORS

After the problem has been identified, factors relevant to resolution ofthe problem are determined. For example, the particular "target patient"will have certain factors of high relevance which need to be explored inorder to better understand the problem facing the target patient.Alternatively, perhaps certain observable factors regarding a drug ordisease may reveal factors which need to be explored.

General information regarding the larger issues to be dealt with arethen researched and discussed. This discussion may include, but is notlimited to, disease experts, clinicians, regulatory experts, marketing,management, etc.

FIG. 6 shows the overall flow of operations for collecting information,developing a representation of the system, building the model, andrunning the model. As indicated by 70, the first step of the processinvolves collecting information from a variety of sources, such aspapers/journals, books, experts, experiments, clinical trials, andinformation developed internally to a company. From this information, aReference Biological Pattern (RBP) is developed and Knowledge Diagrams(KDs) are constructed (72) to represent the collected knowledge. Fromthe RBP and KDs, the interactive model is laid out (74) using themodelling tool and balanced (75) so that model behavior at all levelsmakes sense. Then the model is run (76), checked for accuracy againstthe RBPs and KDs (78), and revised (80), if necessary. Once the model isdetermined to be acurate it is ready for use (83).

If the model does check accurately against the RBP and KDs the model canbe used to generate data that address the question posed at thebeginning of the project. The model can be used for a variety ofpurposes, including drug development and clinical trial development.

CREATION OF THE REFERENCE BIOLOGICAL PATTERN AND KNOWLEDGE DIAGRAMS

The goal in creating the Reference Biological Pattern and KnowledgeDiagrams is to define clinical outcomes of interest, the biologicalsystems involved, and the relevant communication mechanisms betweenbiological systems. The way the relevant biological factors behave overtime and what therapies have been previously tried is also determined.

A RBP is based upon carefully selected experimental, often clinical,data showing what happens in real world situations. When the model iscompleted, it must gives outcomes matching the RBPs. This grounds themodel in the reality of clinically observable outcomes. For example, ifa patient has a middle ear infection, certain biological responsesshould be evident and certain clinical symptoms should be manifested,such as pain, bulging of the eardrum, etc. If the model does notduplicate real life outcomes in the range of interest, then it is notvalid and requires modification.

The information gleaned from literature, books, experiments, internallydeveloped information, and experts is synthesized into the "KnowledgeDiagram". The KD captures the many relationships evidenced in thedisparate sources of information. The KDs are representations of therelevant biological systems and processes and the relationships betweenthem that must be built into the model.

KDs are constructed from elements connected by flow arrows. The flowarrows may have regulator indicators, such as plus and minus, whichinfluence the flow represented by the flow arrow. The KDs incorporatemany levels. The top level defines the disease, focusing on the clinicaloutcomes, biologic factors, and susceptibility factors. These aregrouped into major functional units.

A next level defines in greater detail the key aspects associated withthe disease. This level conveys what biological mechanisms areresponsible for the top level aspects, what initiates the mechanisms,what controls and regulates the mechanisms, what inputs and outputsdefine the system, and what anatomic considerations are involved.

FIG. 7 shows an example of a Knowledge Diagram represented by numeral20. The KD is intended to be an example only, and not biologically ormedically accurate. In general, the diagram shows nodes representingentities and arcs representing interactions/relationships between theentities. The boxes having "+" and "-" therein indicate enhancing orinhibiting the particular interaction shown. This particular KDrepresents the activation process of the basophil cell. The arrowsbetween cell types represents a flow or state change that is regulatedpositively and negatively..

CREATION OF THE MODEL

The model is then created based on the RBP and KDs. Nodes in the KDslabelled as cell types become cell pools in the model. Connectionsbetween cell pools in the model are based on state change links in theKD. These connections provide pathways for the flow of cells from onecell pool, representing one cell type/state, to another.

Connections between cell pools are "regulated," e.g., the number ofcells selected for movement from one cell type/state to another, by thelinks that connect to the "+" and "-" boxes on the links between thecell pools. These controlling items usually designate chemical levelsthat either enhance or inhibit the cell transition between states. Eachcontributor to the transition function, or regulator, is weighted,usually with a value between 0 and 1. The sum of the weights of allcontributors to a regulator usually totals 1. Initially, the weight foreach chemical influencing a transition is assigned equally. Theseweights are then adjusted when the model is balanced. The calculationfor a regulator is performed using a synthesis block. The result is aregulator that represents a percentage of the cells that should make thetransition, and is used as a multiplier on the contents of a cell pool.

Based on the knowledge diagrams, the various cell types produce avariety of chemicals. Production of these chemicals is accomplished inthe model primarily by a synthesis block. The input to the synthesisblock is the quantity of cells of the type producing the chemical, alongwith the values of the various other chemicals that influence productionof the particular chemical by the particular cell type. These chemicalinfluences are weighted as they are in the cell transition links andmust then be balanced. The chemical influences serve, as they do withthe cell transition links, as regulators on the production of thechemicals, enhancing or inhibiting production as appropriate.

Cell pool types and the regulators associated with the cell pool typesare combined into a higher level called a cell pool controller. Sets ofrelated cell pool controllers are combined to form "cell classes." Cellclasses can then be combined to form larger functions, such as theimmune response or bone remodelling.

The Fundamental Model Unit

The general structure of a particular level is a collection offundamental model units. These may be stand-alone model units, whichserve to communicate information to, or receive and synthesizeinformation from, another level. FMUs on a particular level may also beconnected to other FMUs at that level, or connected to both other FMUson the same level and FMUs on other levels.

An FMU can be thought of as a collection of inputs which are synthesizedinto one or more outputs. Examples of typical FMUs are cell classes,cell pool controllers, cell pools and regulators of cell pools.

The inputs may be comprised of virtually any item of informationrelevant to the biology of interest. These include items directlytraceable to the biology, as well as items necessary to accuratelyconvey information regarding the biological items. For example, aquantity of interleukin-1B may be a relevant biological factor at aparticular cell site.

As discussed previously, randomness is made a part of the model viarandom number generators (RNGs). Probability distributions are chosen aspart of the model to closely reflect the underlying distribution of thebiology being modelled at any particular point. A typical probabilitydistribution would be that of a Bell curve. Randomness can be used torepresent natural variance in cellular events such as cell proliferationand apoptosis.

The RNGs can be used at any level of the hierarchy to provide adistribution of a particular type of information. For example, the RNGcould be used to produce multiple patients, multiple groups of patients,multiple types of cellular reactions, etc. By using an RNG, keyvariables within expected biological ranges, having an expectedbiological distribution, can be generated. This provides a model withmuch greater real world accuracy and allows investigators to explorepotential variances.

Internally, to simulate the normal variation between patients with thesame basic characteristics, a random number generator varies certainbiological parameters within their normal biological ranges. Each timethe model is run, even with the exact same parameters, the answer willbe slightly different. Running the model many times will provide anormal variation in clinical response for a given patient type.

Probability distribution functions are used along with the random numbergenerators to further refine models of these dynamic systems. Fuzzylogic is also used as part of the process of manipulating information.Many biological aspects are analogous to the analysis carried out byfuzzy logic. Fuzzy logic is an artificial intelligence technique used tohandle situations where membership in a set is not completely definedbut occurs within some variance level.

Feedback loops are also used to create systems providing an accuraterepresentation of complex biological systems. For example, one may knowA affects B, which affects C, and C affects A. But how does changing Baffect A? By providing the feedback loops which handle theserelationships in a multi-variable, simultaneous fashion, non-intuitiveinsights about diseases, therapies, and other system characteristics canbe provided from the model.

The process, or processes, represented by an FMU, group of FMU levels,or series of levels will all have particular time constraints withinwhich they must operate to be biologically correct. These time framesmay be on the order of fractions of seconds, months, or even years.Recurrent otitis media, for example, has some cycles which happen overhours, and other cycles which happen over months. Subcellularinteractions, on the other hand, may happen in a matter of seconds, orless. FMUs incorporate these time factors by using rates either in theform of an input to a FMU, or within the synthesis performed by the FMU.

Linking Fundamental Model Units on Each Level and Between Levels

As stated previously, the bottom level of a model will typicallyrepresent a cellular or subcellular level. At this level there isusually no identifiable anatomy, only cellular or subcellular entities.These are represented by classes of cells known to be associated with aspecific disease.

The next level in the hierarchy may be a system or anatomic area whichis the primary locality of cells represented at the bottom level, andthe system or anatomy area affected by the disease under study. Forexample, this level may represent the immunological system or an organ,such as the liver.

Further levels may include the larger biology within which the system oranatomic area resides, for example, external patient characteristics,and a next level may be patient populations and characteristics.

Regardless of the level , each level is composed of a plurality of FMUswho's inputs and outputs are linked to simulate the actual interationwithin the biologic system being modelled. Similarly, the FMUs onseparate levels are linked to simulate the complete biologic system.

Model Balancing

Once the model is created in the modelling tool, the model must be run(executed) and "balanced" to create the desired, appropriate behaviors.Balancing is performed at two levels, the cell population level and theoverall model level, and is extremely time consuming and laborintensive. Balancing requires input and knowledge not available orrepresentable in the Knowledge Diagrams because it is this knowledgethat makes the model executable. The balancing process can help topinpoint holes and inconsistencies in available scientific knowledge.

Before and after the model is run, or executed, each fundamental modelunit, group of fundamental model units, level, group of levels, orabstractions crossing these boundaries must be checked againstcorresponding real world entities of information from the RBP, whichincludes the KDs. For example, a particular piece of literature may dealwith a particular biological system which is self contained within aparticular level of the model. This level entity may be checked foraccuracy against the real world information disclosed in the literatureas described in the KDs and RBPs.

Initial cell balancing requires the development of stable cell poolswhen the model is run for each cell class, wherein the cell pools are atappropriate levels for the given conditions. Thus, undernon-inflammatory conditions the cell classes must behave in a stable,healthy manner when the model is executed , while under increasinglychallenging conditions they must either increase or decrease theirnumbers as appropriate and stabilize on a reasonable population count,or "set point." Each cell class must be balanced so that theinterrelationships between the various cell controllers within a cellclass perform properly and the cell pool populations achieve appropriaterelative counts. A reasonable behavior for each cell population mustfirst be obtained because all of the cell populations interrelate andhave feedback among the cell populations. Once appropriate cellpopulation behaviors are achieved for a cell class, the chemicalproduction by the cell population is also balanced and normalized sothat feedback is appropriate for the given condition.

With balanced, well-behaved cell classes, the next step is to establishan appropriately behaved overall model. This involves evaluating theinteractions between cell classes through the chemicals that theyproduce when the model is run. Based on the behavior of the overallmodel when executed, each cell class is re-evaluated and re-balanced sothat the interrelationships generate an overall global behavior thatmatches the clinical baselines, or RBPs, that have been developed. Thishelps to test and validate the model behavior under a variety ofconditions.

As each cell class is re-balanced in the context of the whole model, theoverall model is re-examined and adjusted for balance. Often, a cellclass, already balanced, may need to be revisited and re-balanced, basedon changes in another cell class and the chemicals it generates. Thisprocess of cell and model balancing is a highly iterative process thatbuilds reasonable global model behavior through the development ofappropriate behavior at the lowest levels in the model, the cell pools,on up through the cell controllers and the cell classes.

For example, the model of a healthy system may be run and modified untila balanced and stabilized system is achieved. A stable state is achievedwhen the results at each level and point are consistent with the RBP andKnowledge Diagrams. Chemical reaction rates, cell population growth,cell population diminishment, and barrier crossing rates are examples ofa few of many Fundamental Model Units, or collections of FundamentalModel Units, which must be consistent with the RBP and KnowledgeDiagrams.

Once the model is stabilized, a particular system may be introduced intothe model, and the model is rebalanced to ensure that the model with thenewly introduced system behaves consistent with the real world. Forexample, a "healthy" model representing components typical of anon-inflamed state could first be built and balanced. The system wouldbe made of components relevant to the inflammatory system but operatingin a healthy, noninflammatory manner.

Once the model appears to exhibit reasonable behavior under a variety ofhealth and disease conditions, the values of the biologic outputs arere-interpreted and mapped into values that correlate with actualclinical outcomes. For example, a number between 0 and 100 indicatinginflammation in submucosal tissue is mapped to an appropriatedimensional change in swelling of the tissue. The model is thensystematically run and tested using a set of matrices on which cellpopulation counts and chemical levels are recorded, along with the inputvalues that define the patient data disease level, and possibletherapies. The model is run repeatedly, systematically altering thevarious input data and recording the various internal outputs of themodel, to ensure that not only the clinical outcomes of the model makesense, but the outcomes achieved through cell population and chemicalproduction changes make sense as well. A redesign and/or a re-balancingof certain portions of the model may need to be made at this point toensure proper behavior under the various key situations of interest.

Once the model has generated satisfactory behavior under a variety ofhealthy, diseased, treatment, and patient biologies, it can be deliveredto the targeted users for examination by experts. These individuals dofurther testing and validating, by varying additional input parameters,but usually only checking the clinical outcomes of the model as opposedto all of the various cell populations and chemical levels. As a resultof this testing, recommendations are made for final modifications to themodel. These modifications are made, when possible and desirable, andthe model is ready for use.

It is also contemplated that the model could be self-balanced byincorporating the RBP and KD into a data structure. The data structurewould be referenced by the model after running and checking the modeloutputs against the expected acceptable results indicated in the RBP andKD data structures.

MODEL MAINTENANCE

The model should be maintained to reflect the most up-to-dateinformation available on a particular system. If a new journal articleindicates information which may alter the previous model, the model canbe updated to incorporate these concepts.

CONNECTED MODELS

FIG. 8 shows an example of linking together two models. In theparticular example shown, a Viral Infection Model 108 is linked to aBasophil Model 110. The jagged edge on each of the two models representsthe changing and synthesizing of information common to the two models,as well as the particulars of the overall system which are desired to bestudied. The process for developing the interface 106 to allow the twomodels to work together is similar to that outlined above for creationof a single model. The general overall structure of each respectivemodel is kept intact, but the models are merged through the creation ofwhat could be considered an interface between the two models. Theinterface 106 is essentially a model of the interaction between the twomodels which are being connected. In the present example, the interface106 would be a model of the viral infection/Basophil interface. Whilethe interface 106 of FIG. 8 is shown as a separate element, it should bekept in mind that this is merely representative. In actuality, anyentities between models which are related in some way may need to bealtered to allow for the two models to work together. Thus, interface106 represents changes on a FMU and level basis, as well as creation ofnew FMUs and levels to merge the two models.

Examples of linked models could include, but are not limited to, modelsof physical entities linked with models of other physical entities,models of physical entities linked with models of biological systems,and models of biological systems linked with models of other biologicalsystems

MODEL USES

Drug Development

The model provides a means of collecting into a dynamic executableformat information regarding drug impact at molecular, and other levels,to predict what will happen at the patient level. Drug treatment isinput into the model in terms of the impact on certain biologicalfactors. For example, an antimicrobial could be described in the modelas a means of decreasing bacterial load 30%. The model is then executedand the effects this antimicrobial has on the immune system response,which can in turn influence certain organ responses can be reviewed.These organ responses may cause certain symptoms to remain or disappear.

As a second example, consider a drug such as ibuprofen which reducesprostaglandin levels and in turn impacts a variety of inflammatoryresponses in the model. The lowered level of prostaglandin production issimulated by the model, and the model when executed determines theeffect on the other key systems, ultimately outputting the effect oflower prostaglandin levels at the patient level. From this informationregarding lowered levels of prostaglandin, valuable information aboutdrug behavior and effect may be observed and discovered. The informationmay include typical values, or ranges, of pharmacological effects,pharmacokinetics for timing and dose implications, human clinicalexperience, etc. These are all very helpful in identifying drugs for aparticular disease.

The present invention may be used to assist in the identification andearly assessment of potential targets for new drug development byallowing scientists using the model to explore within a more completesystem from which observations and hypotheses can be made, based onknowledge of the subcellular and cellular levels and their effects onclinical outcomes. The model can identify biologic factors that have thegreatest leverage on clinical outcomes in the particular disease.

Clinical Trial

The model can be run using various patient and treatment characteristicsto determine the patients that would benefit most from specifictreatments and those patients that may experience problems in the study.This would provide optimal patient selection and appropriate designfactors to detect, monitor or handle any negative outcomes.

The model can also evaluate different time lengths for clinical trials,optimal times to take clinical measures, and even the dosing schedulefor the drug. Since it can simulate trial results for any combinations,different clinical trial options can be run.

A user can stipulate the characteristics of an individual patient, orthose characteristics that typify a particular group of patients. Forexample, is the patient a smoker, is there a family history of certaindisease, is the patient compromised with other systemic conditions thateffect outcomes, etc. Then the drug or treatment regimen is input. Themodel is run and the output is the clinical status or clinical resultfrom applying that treatment to that patient or patients.

HUMAN INTERFACE

The human interface of the model provides the user an input to and anoutput from the model. The graphics interface can be customized toreflect the particular application for which a model is being used.Other typical human interface elements, such as keyboards, a mouse,trackballs, touchscreens, printers, etc. can also be used.

COMPUTER SYSTEM

FIG. 9 represents a computer system 40 on which the present inventionmay be practiced. The computer system includes a CPU 10, I/O Adapter 18,Communications Adapter 34, RAM 14, ROM 16, User Interface Adapter 22having connected thereto a Keyboard 24, Mouse 26, and Speaker 28, aDisplay Adapter 36, and Display 38. All elements are connected to Bus12. The computer system shown is merely exemplary, and is not intendedto be limiting. The computer system could be of virtually any size orpower, depending on the particular complexities of the model.

While the invention has been described in terms of a preferredembodiment in a specific system environment, those skilled in the artrecognize that the invention can be practiced, with modification, inother and different hardware and software environments within the spiritand scope of the appended claims.

I claim:
 1. A computer executable model of a hierarchical biologicalsystem, the model for use with a computer system including a memory anda processor, the model comprising:a plurality of biological modellingunits stored in the memory, each biological modelling unit having:aplurality of chemical level inputs, each input representing a level of achemical in an environment containing the biological modelling unit; atleast one chemical production output representing a level of a chemicalproduced by the biological modelling unit and stored as part of theenvironment containing the biological modelling unit; and a chemicalproduction function describing the production output of a chemical bythe biological modelling unit as a function of the chemical levelinputs; and the plurality of biological modelling units organized into aplurality of levels, each level representing a different level ofbiological function, each biological modelling unit in any levelproviding its chemical production outputs to the environment containingthe biological modelling unit, receiving as its chemical level input,chemical levels from the environment, and wherein execution of the modelby the processor modifies the respective inputs and outputs of each ofthe biological modelling units.
 2. The computer executable model ofclaim 1, further comprising:a regulator biological modelling unitrepresenting a regulator function comprising:at least one chemical levelinput representing a level of a chemical in an environment containingthe regulator biological modelling unit; at least one quantity inputrepresenting a quantity of a first type of biological modelling unit inthe environment; and a regulator function describing a change in stateof the first type of biological modelling unit to a second type ofbiological modelling unit as a function of the chemical level andquantity inputs, wherein execution of the model by the processor causesrespective changes in the quantities of the first and second types ofbiological modelling units.
 3. The computer executable model of claim 1,wherein first and second biological modelling units represent first andsecond cell pools, each cell pool representing cells of a particulartype or cells in particular state, the model further comprising:aregulator biological modelling unit representing a regulator functioncomprising:at least one chemical level input representing a level of achemical in an environment containing the regulator biological modellingunit; at least one quantity input representing a quantity of a firsttype of biological modelling unit in the environment; and regulatorfunction describing a change in state of the first type of biologicalmodelling unit to a second type of biological modelling unit as afunction of the chemical level and quantity inputs, wherein execution ofthe model by the processor causes respective changes in the quantitiesof the first and second types of biological modelling units, andrepresenting changes in the quantities of cells in the respective firstand second cell pools.
 4. A computer executable model of a hierarchicalbiological system, the model for use with a computer system including amemory and a processor, the model comprising:a plurality of cell poolmodelling units stored in the memory, each cell pool modelling unitrepresenting a quantity of a cells having a particular cell type or aparticular cell state, each cell type modelling unit having:at least onechemical level input representing a level of a chemical in anenvironment containing the cell pool modelling unit; at least onequantity input representing a quantity of a cells of a selected cellpool modelling unit; and at least one chemical production function,executable by the processor, that outputs a level of a chemical producedby the cell pool modelling unit in response to the chemical level inputand quantity input; wherein execution of the model by the processorcauses the chemical production function of each cell pool modelling unitto modify the output of the level of chemical of produced by the cellpool modelling unit; and a plurality of regulator modelling units storedin the memory, each regulator modelling unit associated with selectedfirst and second cell pool modelling units, and having:at least oneinput of either:a level of a chemical in the environment of the firstcell pool modelling unit; or a quantity of a cell in the environment ofthe first cell pool modelling unit; and a regulator function, executableby the processor, and representing the transition of cells between thefirst and second cell pool modelling units by modifying the quantity ofthe cells represented by the first cell pool modelling unit and thequantity of cells represented by the second cell pool modelling unit;and wherein execution of the model by the processor causes eachregulation function to dynamically modify the quantity of cellsrepresented by each cell pool modelling unit.
 5. A computer executablemodel of a hierarchical biological system, the model for use with acomputer system including a memory and a processor, the modelcomprising:a plurality of cell pool modelling units stored in thememory, each cell pool modelling unit representing a quantity of cellsof a particular type of cell or a particular cell state and having achemical production function describing an output level of a chemicalproduced by the cells of the cell pool modelling unit as a function ofeither a quantity of cells of at least one cell pool modelling or alevel of a chemical; a plurality of cell pool regulator functions,stored in the memory and executable by the processor, each cell poolregulator function regulating the quantities of cells associated withselected ones of the cell pool modelling units in response to inputsincluding at least one of a level of a chemical in an environmentcontaining the cell pool modelling units or a quantity of cellsrepresented by a cell pool modelling unit; a plurality of a cell classmodelling units stored in the memory, each cell class modelling unitassociated with selected ones of the cell pool modelling units andselected ones of the cell pool regulator functions, and representing thebiological functions of a single class of cells; a plurality of systemmodelling units stored in the memory, each system modelling unitassociated with selected ones of the cell class modelling units, andrepresenting a coordinated biological function of an anatomic system;and wherein execution of the model causes the chemical productionfunctions of the cell pool modelling units to dynamically modify thelevels of chemical produced by the cell pool modelling units, and causesthe cell pool regulator functions to dynamically modify the quantitiesof cells represented by the cell pool modelling units.